x— title: “EDA” output: html_document: default word_document: default date: “2023-11-28” —
# Load packages for data manipulation
library(tidyverse)
library(lubridate)
library(ggplot2)
library(cowplot)
library(dplyr)
library(DT)
library(GGally)
library(cluster)
library(factoextra) # for visualization
library(mice)
# Load data
maindata <- read.csv("maindata.csv")
data_monthly <- read.csv("data_monthly.csv")#data preprocessing
# Create a monthly date sequence from January 1990 to November 2023.
dates <- seq(ymd("1990-01-01"), ymd("2023-11-01"), by = "1 month")
# Assume maindata has been loaded into the environment. Now, add the date column.
maindata <- maindata %>%
mutate(date = dates)
# Remove variables with excessive missing values (NA)
maindata <- maindata %>% select(-solarradiation, -uvindex)
# Replace NA values with 0
maindata <- maindata %>%
mutate(
Clear = replace(Clear, is.na(Clear), 0),
Overcast = replace(Overcast, is.na(Overcast), 0),
Partially.cloudy = replace(Partially.cloudy, is.na(Partially.cloudy), 0),
Rain..Partially.cloudy = replace(Rain..Partially.cloudy, is.na(Rain..Partially.cloudy), 0),
Rain..Overcast = replace(Rain..Overcast, is.na(Rain..Overcast), 0),
Rain = replace(Rain, is.na(Rain), 0)
)
# Rename
maindata <- maindata %>%
rename(
'Temperature' = temp,
'Humidity' = humidity,
'Wind Speed' = windspeed ,
'Sea Level Pressure' = sealevelpressure,
'Cloud Cover' = cloudcover,
'Precipitation' = precip,
`Partially Cloudy` = Partially.cloudy,
`Rain & Partially Cloudy` = Rain..Partially.cloudy,
`Rain & Overcast` = Rain..Overcast
)The preprocessing involves creating a monthly date sequence from January 1990 to November 2023, which is then added to maindata. This dataset includes various weather-related variables like Temperature, Humidity, Wind Speed , cloud cover, Sea Level Pressure, Precipitationitation and different conditions (Clear, Overcast, Partially cloudy, Rain & Partially, Rain & Overcast, Rain).longtype
# Melt the data into a long format suitable for ggplot2, excluding 'uvindex' and 'solarradiation' due to NaN values.
maindata_long <- maindata %>%
gather(key = "variable", value = "value", -date) %>%
filter(!is.nan(value))Plot the annual mean Temperature
# Assuming maindata_long is already created with the 'date' and 'value' columns
# and the 'variable' column indicating the type of measurement, such as Temperature.
# First, we will filter out Temperature-related variables only
maindata_Temperature <- maindata_long %>%
filter(variable %in% c("Temperature")) # Replace with actual Temperature variable names
# Extract the year from the date
maindata_Temperature <- maindata_Temperature %>%
mutate(year = year(date))
# Assuming maindata_long is already created with the 'date' and 'value' columns
# and the 'variable' column indicating the type of measurement, such as Temperature.
# First, we will filter out Temperature-related variables only
maindata_Temperature <- maindata_long %>%
filter(variable %in% c("Temperature")) # Replace with actual Temperature variable names
# Extract the year from the date
maindata_Temperature <- maindata_Temperature %>%
mutate(year = year(date))
# Calculate the annual mean Temperature
annual_mean_Temperature <- maindata_Temperature %>%
group_by(year, variable) %>%
summarise(year_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Plot the annual mean Temperature
annual_mean_Temperature_plot <- annual_mean_Temperature %>%
filter(variable == "Temperature") %>%
ggplot(aes(x = year, y = year_mean)) +
geom_point() +
geom_line() +
geom_smooth(method = "loess") +
theme(
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
labs(
title = "Annual Mean Temperature",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Degrees Celsius"
) +
NULL
annual_mean_Temperature_plot
Plot the monthly mean Temperature
# Assuming maindata_long has already been created and includes the 'date' and 'value' columns
# We'll use the 'Temperature' variable for plotting the mean Temperature
# First, extract the year and month from the date
maindata_Temperature <- maindata_long %>%
filter(variable == "Temperature") %>%
mutate(year = year(date), month = month(date, label = TRUE))
# Calculate the monthly mean Temperature
monthly_mean_Temperature <- maindata_Temperature %>%
group_by(year, month) %>%
summarise(month_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Define a colour scale for the months
my_colour <- scales::hue_pal()(12)
# Plot the monthly mean Temperature
Temperature_plot <- ggplot(monthly_mean_Temperature, aes(x = year, y = month_mean, colour = month)) +
geom_point(size = 0.5) +
geom_smooth(method = "loess") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
scale_color_manual(values = my_colour) +
labs(
title = "Monthly Mean Temperature",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Degrees Celsius"
) +
facet_wrap(~month) +
NULL
Temperature_plot
Plot the annual mean Humidity
maindata_Humidity<- maindata_long %>%
filter(variable %in% c("Humidity"))
# Extract the year from the date
maindata_Humidity <- maindata_Humidity %>%
mutate(year = year(date))
# First, we will filter out Temperature-related variables only
maindata_Humidity <- maindata_long %>%
filter(variable %in% c("Humidity"))
# Extract the year from the date
maindata_Humidity <- maindata_Humidity %>%
mutate(year = year(date))
# Calculate the annual mean Humidity
annual_mean_Humidity <- maindata_Humidity %>%
group_by(year, variable) %>%
summarise(year_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Plot the annual mean Humidity with the title and subtitle centered
annual_mean_Humidity_plot <- annual_mean_Humidity %>%
filter(variable == "Humidity") %>%
ggplot(aes(x = year, y = year_mean)) +
geom_point() +
geom_line() +
geom_smooth(method = "loess") +
theme(
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
labs(
title = "Annual Mean Humidity",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "%"
) +
NULL
annual_mean_Humidity_plot
Plot the monthly mean Humidity
# First, extract the year and month from the date
maindata_Humidity <- maindata_long %>%
filter(variable == "Humidity") %>%
mutate(year = year(date), month = month(date, label = TRUE))
# Calculate the monthly mean Temperature
monthly_mean_Humidity <- maindata_Humidity %>%
group_by(year, month) %>%
summarise(month_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Define a colour scale for the months
my_colour <- scales::hue_pal()(12)
# Plot the monthly mean Temperature
Humidity_plot <- ggplot(monthly_mean_Humidity, aes(x = year, y = month_mean, colour = month)) +
geom_point(size = 0.5) +
geom_smooth(method = "loess") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
scale_color_manual(values = my_colour) +
labs(
title = "Monthly Mean Humidity",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Degrees Celsius"
) +
facet_wrap(~month) +
NULL
Humidity_plot
Plot the annual mean Wind Speed
maindata_WindSpeed <- maindata_long %>%
filter(variable %in% c("Wind Speed"))
# Extract the year from the date
maindata_WindSpeed <- maindata_WindSpeed %>%
mutate(year = year(date))
# First, we will filter out Temperature-related variables only
maindata_WindSpeed <- maindata_long %>%
filter(variable %in% c("Wind Speed"))
# Extract the year from the date
maindata_WindSpeed <- maindata_WindSpeed %>%
mutate(year = year(date))
# Calculate the annual mean Humidity
annual_mean_WindSpeed <- maindata_WindSpeed %>%
group_by(year, variable) %>%
summarise(year_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Plot the annual mean Wind Speed with the title and subtitle centered
annual_mean_WindSpeed_plot <- annual_mean_WindSpeed %>%
filter(variable == "Wind Speed") %>%
ggplot(aes(x = year, y = year_mean)) +
geom_point() +
geom_line() +
geom_smooth(method = "loess") +
theme(
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
labs(
title = "Annual Mean Wind Speed",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Kilometers per hour (kph) "
) +
NULL
annual_mean_WindSpeed_plot
Plot the monthly mean Wind Speed
# First, extract the year and month from the date
maindata_WindSpeed<- maindata_long %>%
filter(variable == "Wind Speed") %>%
mutate(year = year(date), month = month(date, label = TRUE))
# Calculate the monthly mean Temperature
monthly_mean_WindSpeed <- maindata_WindSpeed %>%
group_by(year, month) %>%
summarise(month_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Define a colour scale for the months
my_colour <- scales::hue_pal()(12)
# Plot the monthly mean Wind Speed
WindSpeed_plot <- ggplot(monthly_mean_WindSpeed , aes(x = year, y = month_mean, colour = month)) +
geom_point(size = 0.5) +
geom_smooth(method = "loess") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
scale_color_manual(values = my_colour) +
labs(
title = "Monthly Mean Wind Speed",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Kilometers per hour (kph) "
) +
facet_wrap(~month) +
NULL
WindSpeed_plot
Plot the annual mean Cloud Cover
maindata_CloudCover<- maindata_long %>%
filter(variable %in% c("Cloud Cover"))
# Extract the year from the date
maindata_CloudCover <- maindata_CloudCover %>%
mutate(year = year(date))
# First, we will filter out Temperature-related variables only
maindata_CloudCover <- maindata_long %>%
filter(variable %in% c("Cloud Cover"))
# Extract the year from the date
maindata_CloudCover <- maindata_CloudCover %>%
mutate(year = year(date))
# Calculate the annual mean Cloud Cover
annual_mean_CloudCover <- maindata_CloudCover %>%
group_by(year, variable) %>%
summarise(year_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Plot the annual mean Cloud Cover with the title and subtitle centered
annual_mean_CloudCover_plot <- annual_mean_CloudCover %>%
filter(variable == "Cloud Cover") %>%
ggplot(aes(x = year, y = year_mean)) +
geom_point() +
geom_line() +
geom_smooth(method = "loess") +
theme(
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
labs(
title = "Annual Mean Cloud Cover",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "% "
) +
NULL
annual_mean_CloudCover_plot
Plot the monthly mean Cloud Cover
# First, extract the year and month from the date
maindata_CloudCover <- maindata_long %>%
filter(variable == "Cloud Cover") %>%
mutate(year = year(date), month = month(date, label = TRUE))
# Calculate the monthly mean Temperature
monthly_mean_CloudCover <- maindata_CloudCover %>%
group_by(year, month) %>%
summarise(month_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Define a colour scale for the months
my_colour <- scales::hue_pal()(12)
# Plot the monthly mean Temperature
CloudCover_plot <- ggplot(monthly_mean_CloudCover, aes(x = year, y = month_mean, colour = month)) +
geom_point(size = 0.5) +
geom_smooth(method = "loess") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
scale_color_manual(values = my_colour) +
labs(
title = "Monthly Mean Cloud Cover",
subtitle = "Data from 1990 - 2023",
y = "%"
) +
facet_wrap(~month) +
NULL
CloudCover_plot
Plot the annual mean Sea Level Pressure
maindata_SeaLevelPressure<- maindata_long %>%
filter(variable %in% c("Sea Level Pressure"))
# Extract the year from the date
maindata_SeaLevelPressure <- maindata_SeaLevelPressure %>%
mutate(year = year(date))
# First, we will filter out Temperature-related variables only
maindata_SeaLevelPressure <- maindata_long %>%
filter(variable %in% c("Sea Level Pressure"))
# Extract the year from the date
maindata_SeaLevelPressure <- maindata_SeaLevelPressure %>%
mutate(year = year(date))
# Calculate the annual mean Humidity
annual_mean_SeaLevelPressure <- maindata_SeaLevelPressure %>%
group_by(year, variable) %>%
summarise(year_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Plot the annual mean Sea Level Pressure with the title and subtitle centered
annual_mean_SeaLevelPressure_plot <- annual_mean_SeaLevelPressure %>%
filter(variable == "Sea Level Pressure") %>%
ggplot(aes(x = year, y = year_mean)) +
geom_point() +
geom_line() +
geom_smooth(method = "loess") +
theme(
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
labs(
title = "Annual Mean Sea Level Pressure",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Millibars (mb)"
) +
NULL
annual_mean_SeaLevelPressure_plot
Plot the monthly mean Sea Level Pressure
# First, extract the year and month from the date
maindata_SeaLevelPressure <- maindata_long %>%
filter(variable == "Sea Level Pressure") %>%
mutate(year = year(date), month = month(date, label = TRUE))
# Calculate the monthly mean Temperature
monthly_mean_SeaLevelPressure <- maindata_SeaLevelPressure %>%
group_by(year, month) %>%
summarise(month_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Define a colour scale for the months
my_colour <- scales::hue_pal()(12)
# Plot the monthly mean Temperature
SeaLevelPressure_plot <- ggplot(monthly_mean_SeaLevelPressure, aes(x = year, y = month_mean, colour = month)) +
geom_point(size = 0.5) +
geom_smooth(method = "loess") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
scale_color_manual(values = my_colour) +
labs(
title = "Monthly Mean Sea Level Pressure",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Millibars (mb)"
) +
facet_wrap(~month) +
NULL
SeaLevelPressure_plot
Plot the annual mean Precipitation
maindata_Precipitation<- maindata_long %>%
filter(variable %in% c("Precipitation"))
# Extract the year from the date
maindata_Precipitation <- maindata_Precipitation %>%
mutate(year = year(date))
# First, we will filter out Temperature-related variables only
maindata_Precipitation <- maindata_long %>%
filter(variable %in% c("Precipitation"))
# Extract the year from the date
maindata_Precipitation <- maindata_Precipitation %>%
mutate(year = year(date))
# Calculate the annual mean Humidity
annual_mean_Precipitation <- maindata_Precipitation %>%
group_by(year, variable) %>%
summarise(year_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Plot the annual mean Precipitation with the title and subtitle centered
annual_mean_Precipitation_plot <- annual_mean_Precipitation %>%
filter(variable == "Precipitation") %>%
ggplot(aes(x = year, y = year_mean)) +
geom_point() +
geom_line() +
geom_smooth(method = "loess") +
theme(
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
labs(
title = "Annual Mean Precipitationitation",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "mm"
) +
NULL
annual_mean_Precipitation_plot
Plot the monthly mean Precipitation
# First, extract the year and month from the date
maindata_Precipitation <- maindata_long %>%
filter(variable == "Precipitation") %>%
mutate(year = year(date), month = month(date, label = TRUE))
# Calculate the monthly mean Temperature
monthly_mean_Precipitation <- maindata_Precipitation %>%
group_by(year, month) %>%
summarise(month_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Define a colour scale for the months
my_colour <- scales::hue_pal()(12)
# Plot the monthly mean Temperature
Precipitation_plot <- ggplot(monthly_mean_Precipitation, aes(x = year, y = month_mean, colour = month)) +
geom_point(size = 0.5) +
geom_smooth(method = "loess") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
scale_color_manual(values = my_colour) +
labs(
title = "Monthly Precipitationitation",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "mm"
) +
facet_wrap(~month) +
NULL
Precipitation_plot
Plot the annual mean conditions: ‘Clear’, ‘Overcast’, ‘Partially cloudy’,‘Rain & Partially cloudy’, ‘Rain & Overcast’, ‘Rain’
# First, we will filter out condition-related variables only
maindata_condition <- maindata_long %>%
filter(variable %in%
c("Clear","Overcast","Partially Cloudy",
"Rain & Partially Cloudy", "Rain & Overcast", "Rain"))
# Extract the year from the date
maindata_condition <- maindata_condition %>%
mutate(year = year(date))
# Calculate the annual mean frequency
annual_mean_condition <- maindata_condition %>%
group_by(year, variable) %>%
summarise(year_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Plot the annual mean frequency with the title and subtitle centered
annual_mean_condition_plot <- annual_mean_condition %>%
ggplot(aes(x = year, y = year_mean)) +
geom_point() +
geom_line() +
geom_smooth(method = "loess") +
facet_wrap(~variable) +
theme(
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
labs(
title = "Annual Mean Frequency of Weather Condition",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Count"
) +
NULL
annual_mean_condition_plot
Plot the monthly mean conditions:
# First, extract the year and month from the date
maindata_condition <- maindata_long %>%
filter(variable %in%
c("Clear","Overcast","Partially Cloudy",
"Rain & Partially Cloudy", "Rain & Overcast", "Rain")) %>%
mutate(year = year(date), month = month(date, label = TRUE))
# Calculate the monthly mean frequency
monthly_mean_condition <- maindata_condition %>%
group_by(variable, year, month) %>%
summarise(month_mean = mean(value, na.rm = TRUE)) %>%
ungroup()
# Define a colour scale for the months
my_colour <- scales::hue_pal()(12)
# Plot
condition_plot <- ggplot(monthly_mean_condition, aes(x = year, y = month_mean, colour = month)) +
geom_point(size = 0.5) +
geom_smooth(method = "loess") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5)
) +
scale_color_manual(values = my_colour) +
labs(
title = "Monthly Frequency of Weather Condition",
subtitle = "Data from Jan. 1990 to Nov. 2023",
y = "Count"
) +
facet_grid(variable~month) +
NULL
condition_plot
Boxplot: Temperature
Temperature_data <- maindata_long %>%
filter(variable == "Temperature") %>%
mutate(month = factor(month(date, label = TRUE))) # Converting the date to a month factor
# Creating the boxplot for each month
Temperature_boxplot <- ggplot(Temperature_data, aes(x = month, y = value)) +
geom_boxplot() +
labs(title = "Monthly Boxplots of Temperature",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "Degrees Celsius") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(Temperature_boxplot)
Boxplot: Temperature with outlier
Temperature_data <- maindata_long %>%
filter(variable == "Temperature") %>%
mutate(year = as.character(year(date)),
month = factor(month(date, label = TRUE)))
outliers <- Temperature_data %>%
group_by(month) %>%
summarise(lower = quantile(value, 0.25) - 1.5 * IQR(value),
upper = quantile(value, 0.75) + 1.5 * IQR(value)) %>%
left_join(Temperature_data, by = "month") %>%
filter(value < lower | value > upper)
Temperature_boxplot_outlier <- ggplot(Temperature_data, aes(x = month, y = value)) +
geom_boxplot() +
geom_text(data = outliers, aes(label = year), vjust = -0.5) +
labs(title = "Monthly Boxplots of Temperature",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "Degrees Celsius") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(Temperature_boxplot_outlier)
Boxplot: Humidity
Humidity_data <- maindata_long %>%
filter(variable == "Humidity") %>%
mutate(month = factor(month(date, label = TRUE))) # Converting the date to a month factor
# Creating the boxplot for each month
Humidity_boxplot <- ggplot(Humidity_data, aes(x = month, y = value)) +
geom_boxplot() +
labs(title = "Monthly Boxplots of Humidity",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "%") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(Humidity_boxplot)
## Boxplot: Humidity with outlier
Humidity_data <- maindata_long %>%
filter(variable == "Humidity") %>%
mutate(year = as.character(year(date)),
month = factor(month(date, label = TRUE)))
outliers <- Humidity_data %>%
group_by(month) %>%
summarise(lower = quantile(value, 0.25) - 1.5 * IQR(value),
upper = quantile(value, 0.75) + 1.5 * IQR(value)) %>%
left_join(Humidity_data, by = "month") %>%
filter(value < lower | value > upper)
Humidity_boxplot_outlier <- ggplot(Humidity_data, aes(x = month, y = value)) +
geom_boxplot() +
geom_text(data = outliers, aes(label = year), vjust = -0.5) +
labs(title = "Monthly Boxplots of Humidity",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "%") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(Humidity_boxplot_outlier)
Boxplot: Wind Speed
WindSpeed_data <- maindata_long %>%
filter(variable == "Wind Speed") %>%
mutate(month = factor(month(date, label = TRUE))) # Converting the date to a month factor
# Creating the boxplot for each month
WindSpeed_boxplot <- ggplot( WindSpeed_data, aes(x = month, y = value)) +
geom_boxplot() +
labs(title = "Monthly Boxplots of Wind Speed",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "Kilometers per hour (kph)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(WindSpeed_boxplot)
Boxplot: Wind Speed with outlier
WindSpeed_data <- maindata_long %>%
filter(variable == "Wind Speed") %>%
mutate(year = as.character(year(date)),
month = factor(month(date, label = TRUE)))
outliers <- WindSpeed_data %>%
group_by(month) %>%
summarise(lower = quantile(value, 0.25) - 1.5 * IQR(value),
upper = quantile(value, 0.75) + 1.5 * IQR(value)) %>%
left_join( WindSpeed_data, by = "month") %>%
filter(value < lower | value > upper)
WindSpeed_boxplot_outlier <- ggplot( WindSpeed_data, aes(x = month, y = value)) +
geom_boxplot() +
geom_text(data = outliers, aes(label = year), vjust = -0.5) +
labs(title = "Monthly Boxplots of Wind Speed",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "Kilometers per hour (kph)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(WindSpeed_boxplot_outlier)
Boxplot: Cloud Cover
CloudCover_data <- maindata_long %>%
filter(variable == "Cloud Cover") %>%
mutate(month = factor(month(date, label = TRUE))) # Converting the date to a month factor
# Creating the boxplot for each month
CloudCover_boxplot <- ggplot(CloudCover_data, aes(x = month, y = value)) +
geom_boxplot() +
labs(title = "Monthly Boxplots of Cloud Cover",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "%") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(CloudCover_boxplot)
Boxplot: Cloud Cover with outlier
CloudCover_data <- maindata_long %>%
filter(variable == "Cloud Cover") %>%
mutate(year = as.character(year(date)),
month = factor(month(date, label = TRUE)))
outliers <- CloudCover_data %>%
group_by(month) %>%
summarise(lower = quantile(value, 0.25) - 1.5 * IQR(value),
upper = quantile(value, 0.75) + 1.5 * IQR(value)) %>%
left_join(CloudCover_data, by = "month") %>%
filter(value < lower | value > upper)
CloudCover_boxplot_outlier <- ggplot(CloudCover_data, aes(x = month, y = value)) +
geom_boxplot() +
geom_text(data = outliers, aes(label = year), vjust = -0.5) +
labs(title = "Monthly Boxplots of Cloud Cover",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "%") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(CloudCover_boxplot_outlier)
Boxplot: Sea Level Pressure
SeaLevelPressure_data <- maindata_long %>%
filter(variable == "Sea Level Pressure") %>%
mutate(month = factor(month(date, label = TRUE))) # Converting the date to a month factor
# Creating the boxplot for each month
SeaLevelPressure_boxplot <- ggplot(SeaLevelPressure_data, aes(x = month, y = value)) +
geom_boxplot() +
labs(title = "Monthly Boxplots of Sea Level Pressure",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "Millibars (mb)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(SeaLevelPressure_boxplot)
Boxplot: Sea Level Pressure with outlier
SeaLevelPressure_data <- maindata_long %>%
filter(variable == "Sea Level Pressure") %>%
mutate(year = as.character(year(date)),
month = factor(month(date, label = TRUE)))
outliers <- SeaLevelPressure_data %>%
group_by(month) %>%
summarise(lower = quantile(value, 0.25) - 1.5 * IQR(value),
upper = quantile(value, 0.75) + 1.5 * IQR(value)) %>%
left_join(SeaLevelPressure_data, by = "month") %>%
filter(value < lower | value > upper)
SeaLevelPressure_boxplot_outlier <- ggplot(SeaLevelPressure_data, aes(x = month, y = value)) +
geom_boxplot() +
geom_text(data = outliers, aes(label = year), vjust = -0.5) +
labs(title = "Monthly Boxplots of Sea Level Pressure",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "Millibars (mb)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(SeaLevelPressure_boxplot_outlier)
Boxplot: Precipitation
Precipitation_data <- maindata_long %>%
filter(variable == "Precipitation") %>%
mutate(month = factor(month(date, label = TRUE))) # Converting the date to a month factor
# Creating the boxplot for each month
Precipitation_boxplot <- ggplot(Precipitation_data, aes(x = month, y = value)) +
geom_boxplot() +
labs(title = "Monthly Boxplots of Precipitation",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "mm") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(Precipitation_boxplot)
Boxplot: Precipitation with Precipitation
Precipitation_data <- maindata_long %>%
filter(variable == "Precipitation") %>%
mutate(year = as.character(year(date)),
month = factor(month(date, label = TRUE)))
outliers <- Precipitation_data %>%
group_by(month) %>%
summarise(
lower = quantile(value, 0.25, na.rm = TRUE) - 1.5 * IQR(value, na.rm = TRUE),
upper = quantile(value, 0.75, na.rm = TRUE) + 1.5 * IQR(value, na.rm = TRUE)
) %>%
left_join(Precipitation_data, by = "month") %>%
filter(value < lower | value > upper)
Precipitation_boxplot_outlier <- ggplot(Precipitation_data, aes(x = month, y = value)) +
geom_boxplot() +
geom_text(data = outliers, aes(label = year), vjust = -0.5) +
labs(title = "Monthly Boxplots of Precipitation",
subtitle = "Data from Jan. 1990 to Nov. 2023",
x = "Month",
y = "mm") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
print(Precipitation_boxplot_outlier)
Yearly Basic Statistics Table (excluding the condition variables)
feture: Temperature, Humidity, Wind Speed , Cloud Cover, Sea Level Pressure, Precipitation
If you want to summarize the data by month instead of by year, you can add ‘group_by(year, month)’
library(DT)
maindata %>%
mutate(year = year(date), month = month(date, label = TRUE)) -> data_summary
data_summary %>%
select('year', 'Temperature', 'Humidity', 'Wind Speed', 'Cloud Cover', 'Sea Level Pressure', 'Precipitation') %>%
group_by(year) %>%
summarise(across(everything(),
list(min = ~min(., na.rm = TRUE),
max = ~max(., na.rm = TRUE),
mean = ~mean(., na.rm = TRUE)))) %>%
mutate_all(~round(., 3)) -> datatable1
datatable(datatable1, options = list(autoWidth = FALSE, scrollX = TRUE))Yearly Basic Statistics Table (only the condition variables)
If you want to summarize the data by month instead of by year, you can add ‘group_by(year, month)’
data_summary %>%
select(Clear:month) %>%
group_by(year) %>%
summarise_at(vars(Clear:Rain),
list(min = ~min(., na.rm = TRUE),
max = ~max(., na.rm = TRUE),
mean = ~mean(., na.rm = TRUE))) %>%
mutate_all(~ifelse(is.finite(.), ., NA)) %>%
mutate_all(~round(., 3)) -> datatable2
datatable(datatable2, options = list(autoWidth = FALSE, scrollX = TRUE))Yearly Basic Statistics Table
If you want to summarize the data by month instead of by year, you can add ‘group_by(year, month)’
# Joining the two tables on the 'year' column
final_table <- left_join(datatable1, datatable2, by = "year")
# Displaying the combined table
datatable(final_table, options = list(autoWidth = FALSE, scrollX = TRUE))#Excluding the variables ‘Clear’, ‘Overcast’, ‘Partially Cloudy’, ‘Rain & Partially Cloudy’, ‘Rain & Overcast’, ‘Rain’, as they make the data look too messy. Should I add them back? # If you want to summarize the data by month instead of by year, you can add ‘group_by(year, month)’
# heatmap(Excluding the 'Clear', 'Overcast', 'Partially Cloudy','Rain & Partially Cloudy', 'Rain & Overcast', 'Rain')
data_summary %>%
select('Temperature', 'Humidity', 'Wind Speed', 'Cloud Cover', 'Sea Level Pressure', 'Precipitation') -> cordata
library(GGally)
ggpairs(cordata)
cordata %>%
cor(use = "complete.obs") -> cor_matrix
heatmap(cor_matrix, Colv=NA, Rowv = NA, scale = "column") 
library(corrplot)
corrplot(cor_matrix, method = 'color', type = 'lower', order = 'hclust',
tl.col = 'black', addCoef.col = 'black', number.cex = 0.6,
cl.ratio = 0.2, tl.srt = 45, col = COL2('RdBu', 10))
## leave blank on non-significant coefficient
## add significant correlation coefficients
testRes = cor.mtest(cordata, conf.level = 0.95)
corrplot(cor_matrix, p.mat = testRes$p,
method = 'color', type = 'lower', insig='blank',
tl.col = 'black', addCoef.col = 'black', number.cex = 0.6,
order = 'AOE', diag=FALSE)